EMA intervention analyses
Dani Cosme
2023-07-19
This code reproduces the EMA intervention analyses reported in the following manuscript:
load packages
library(pacman)
pacman::p_load(tidyverse, brms, ggeffects, kableExtra, tidybayes, install = TRUE)define aesthetics
palette = c("#e64626", "#1985a1", "#4c5c68", "#FAC748")
plot_aes = theme_minimal() +
theme(legend.position = "top",
legend.text = element_text(size = 16),
text = element_text(size = 18, family = "Futura Medium"),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.text = element_text(color = "black"),
axis.line = element_line(colour = "black"),
axis.ticks.y = element_blank())define functions
make_table = function(data) {
data %>%
broom.mixed::tidy(conf.int = TRUE) %>%
filter(effect == "fixed") %>%
mutate(term = gsub("\\(Intercept\\)", "intercept", term),
term = gsub("regulation_expression", "signature expression", term),
term = gsub("active_weekon", "intervention week (active)", term),
term = gsub("active_weekoff", "intervention week (control)", term),
term = gsub("signal_count", "signal", term),
term = gsub(":", " x ", term),
`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", estimate, conf.low, conf.high)) %>%
select(term, `b [95% CI]`) %>%
knitr::kable(digits = 2)
}load data
merged = read.csv("../data/task_neuro_data.csv", stringsAsFactors = FALSE)
ema = read.csv("../data/ema.csv")merge and prep for modeling
between = merged %>%
filter(condition == "mindful attention") %>%
select(pID, dot, trial_cond, condition) %>%
group_by(pID, trial_cond, condition) %>%
summarize(dot_between = mean(dot, na.rm = TRUE)) %>%
group_by(trial_cond) %>%
mutate(dot_between_c = scale(dot_between, scale = FALSE, center = TRUE)) %>%
mutate(sd_dot = sd(dot_between, na.rm = TRUE),
dot_between_std = dot_between_c / sd_dot) %>%
select(pID, condition, trial_cond, dot_between_std) %>%
mutate(trial_cond = sprintf("%s_expression", trial_cond)) %>%
spread(trial_cond, dot_between_std)
ema_within = ema %>%
left_join(., between)descriptives
weekly drinking
ema_within %>%
mutate(week = ifelse(signal_count %in% c(1:14), 1,
ifelse(signal_count %in% c(15:28), 2,
ifelse(signal_count %in% c(29:42), 3, 4)))) %>%
group_by(pID, week) %>%
summarize(sum = sum(drinks_number_noc, na.rm = TRUE)) %>%
ungroup() %>%
summarize(min = min(sum),
max = max(sum),
mean = mean(sum),
sd = sd(sum)) %>%
kable(digits = 1, format = "pandoc")| min | max | mean | sd |
|---|---|---|---|
| 0 | 45 | 5.3 | 7 |
effectiveness and individual differences: experience sampling intervention analyses (H3-4)
✅ H3a: Active intervention weeks will increase participants’ self-reported mindful attention to alcohol
✅ H3b: Mindful attention will be positively associated with decreased alcohol consumption
✅ H3c: There will be an indirect effect of intervention-related change in alcohol consumption through greater mindful attention to alcohol
✅ H4a: People with stronger signature expression would show greater increases in mindful attention to alcohol on active intervention weeks compared to control weeks
✅ H4b: People with stronger signature expression would show more negative relationships between mindful attention to alcohol and alcohol consumption
prior = c(prior(normal(0, 1), class=b))run model
table
fit_brm_m %>%
broom.mixed::tidy(conf.int = TRUE) %>%
filter(effect == "fixed") %>%
mutate(term = gsub("\\(Intercept\\)", "intercept", term),
term = gsub("regulation_expression", "signature expression", term),
term = gsub("active_weekon", "intervention week (active)", term),
term = gsub("active_weekoff", "intervention week (control)", term),
term = gsub("mindful_response", "mindful response", term),
response = gsub("mindfulscale", "mindful response", response),
response = gsub("drinksnumber", "number of drinks", response),
term = gsub(":", " x ", term),
`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", estimate, conf.low, conf.high)) %>%
rename("outcome" = response) %>%
select(outcome, term, `b [95% CI]`) %>%
arrange(outcome) %>%
knitr::kable(digits = 2, format = "pandoc")| outcome | term | b [95% CI] |
|---|---|---|
| mindfulresponse | intercept | -0.21 [-0.38, -0.07] |
| mindfulresponse | intervention week (active) | 0.48 [0.26, 0.73] |
| mindfulresponse | signature expression | -0.14 [-0.29, 0.02] |
| mindfulresponse | intervention week (active) x signature expression | 0.41 [0.17, 0.63] |
| number of drinks | intercept | 1.31 [0.92, 1.73] |
| number of drinks | intervention week (active) | 0.10 [-0.45, 0.66] |
| number of drinks | signature expression | -0.29 [-0.58, 0.01] |
| number of drinks | mindful response | -0.59 [-0.98, -0.17] |
| number of drinks | signature expression x mindful response | -0.33 [-0.73, 0.08] |
indirect effect test
hypothesis(
fit_brm_m,
'b_drinksnumber_mindful_response * b_mindfulresponse_active_weekon + cor_pID__mindfulresponse_active_weekon__drinksnumber_active_weekon * sd_pID__mindfulresponse_active_weekon * sd_pID__drinksnumber_mindful_response = 0',
class = NULL,
seed = 6523
)## Hypothesis Tests for class :
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (b_drinksnumber_m... = 0 -0.26 0.13 -0.52 -0.01 NA
## Post.Prob Star
## 1 NA *
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.summary
summary(fit_brm_m)## Family: MV(gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: mindful_response ~ active_week * regulation_expression + (0 + active_week | i | pID)
## drinks_number ~ active_week + regulation_expression * mindful_response + (0 + active_week + mindful_response | i | pID)
## Data: ema_within (Number of observations: 340)
## Draws: 4 chains, each with iter = 500; warmup = 250; thin = 4;
## total post-warmup draws = 250
##
## Group-Level Effects:
## ~pID (Number of levels: 31)
## Estimate
## sd(mindfulresponse_active_weekoff) 0.14
## sd(mindfulresponse_active_weekon) 0.16
## sd(drinksnumber_active_weekoff) 0.46
## sd(drinksnumber_active_weekon) 0.75
## sd(drinksnumber_mindful_response) 0.78
## cor(mindfulresponse_active_weekoff,mindfulresponse_active_weekon) -0.29
## cor(mindfulresponse_active_weekoff,drinksnumber_active_weekoff) 0.02
## cor(mindfulresponse_active_weekon,drinksnumber_active_weekoff) 0.00
## cor(mindfulresponse_active_weekoff,drinksnumber_active_weekon) -0.08
## cor(mindfulresponse_active_weekon,drinksnumber_active_weekon) 0.17
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 0.27
## cor(mindfulresponse_active_weekoff,drinksnumber_mindful_response) -0.29
## cor(mindfulresponse_active_weekon,drinksnumber_mindful_response) 0.28
## cor(drinksnumber_active_weekoff,drinksnumber_mindful_response) 0.10
## cor(drinksnumber_active_weekon,drinksnumber_mindful_response) -0.08
## Est.Error
## sd(mindfulresponse_active_weekoff) 0.08
## sd(mindfulresponse_active_weekon) 0.10
## sd(drinksnumber_active_weekoff) 0.29
## sd(drinksnumber_active_weekon) 0.30
## sd(drinksnumber_mindful_response) 0.24
## cor(mindfulresponse_active_weekoff,mindfulresponse_active_weekon) 0.40
## cor(mindfulresponse_active_weekoff,drinksnumber_active_weekoff) 0.39
## cor(mindfulresponse_active_weekon,drinksnumber_active_weekoff) 0.39
## cor(mindfulresponse_active_weekoff,drinksnumber_active_weekon) 0.38
## cor(mindfulresponse_active_weekon,drinksnumber_active_weekon) 0.38
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 0.38
## cor(mindfulresponse_active_weekoff,drinksnumber_mindful_response) 0.36
## cor(mindfulresponse_active_weekon,drinksnumber_mindful_response) 0.36
## cor(drinksnumber_active_weekoff,drinksnumber_mindful_response) 0.36
## cor(drinksnumber_active_weekon,drinksnumber_mindful_response) 0.33
## l-95% CI
## sd(mindfulresponse_active_weekoff) 0.01
## sd(mindfulresponse_active_weekon) 0.01
## sd(drinksnumber_active_weekoff) 0.03
## sd(drinksnumber_active_weekon) 0.11
## sd(drinksnumber_mindful_response) 0.30
## cor(mindfulresponse_active_weekoff,mindfulresponse_active_weekon) -0.89
## cor(mindfulresponse_active_weekoff,drinksnumber_active_weekoff) -0.75
## cor(mindfulresponse_active_weekon,drinksnumber_active_weekoff) -0.73
## cor(mindfulresponse_active_weekoff,drinksnumber_active_weekon) -0.76
## cor(mindfulresponse_active_weekon,drinksnumber_active_weekon) -0.67
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) -0.54
## cor(mindfulresponse_active_weekoff,drinksnumber_mindful_response) -0.86
## cor(mindfulresponse_active_weekon,drinksnumber_mindful_response) -0.52
## cor(drinksnumber_active_weekoff,drinksnumber_mindful_response) -0.63
## cor(drinksnumber_active_weekon,drinksnumber_mindful_response) -0.67
## u-95% CI Rhat
## sd(mindfulresponse_active_weekoff) 0.33 1.00
## sd(mindfulresponse_active_weekon) 0.35 1.00
## sd(drinksnumber_active_weekoff) 1.12 1.00
## sd(drinksnumber_active_weekon) 1.31 1.00
## sd(drinksnumber_mindful_response) 1.28 1.01
## cor(mindfulresponse_active_weekoff,mindfulresponse_active_weekon) 0.60 1.00
## cor(mindfulresponse_active_weekoff,drinksnumber_active_weekoff) 0.73 1.00
## cor(mindfulresponse_active_weekon,drinksnumber_active_weekoff) 0.73 1.00
## cor(mindfulresponse_active_weekoff,drinksnumber_active_weekon) 0.66 1.00
## cor(mindfulresponse_active_weekon,drinksnumber_active_weekon) 0.81 1.00
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 0.85 1.00
## cor(mindfulresponse_active_weekoff,drinksnumber_mindful_response) 0.51 1.00
## cor(mindfulresponse_active_weekon,drinksnumber_mindful_response) 0.85 1.01
## cor(drinksnumber_active_weekoff,drinksnumber_mindful_response) 0.74 1.00
## cor(drinksnumber_active_weekon,drinksnumber_mindful_response) 0.56 1.00
## Bulk_ESS
## sd(mindfulresponse_active_weekoff) 632
## sd(mindfulresponse_active_weekon) 600
## sd(drinksnumber_active_weekoff) 748
## sd(drinksnumber_active_weekon) 551
## sd(drinksnumber_mindful_response) 541
## cor(mindfulresponse_active_weekoff,mindfulresponse_active_weekon) 741
## cor(mindfulresponse_active_weekoff,drinksnumber_active_weekoff) 817
## cor(mindfulresponse_active_weekon,drinksnumber_active_weekoff) 1000
## cor(mindfulresponse_active_weekoff,drinksnumber_active_weekon) 626
## cor(mindfulresponse_active_weekon,drinksnumber_active_weekon) 801
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 791
## cor(mindfulresponse_active_weekoff,drinksnumber_mindful_response) 443
## cor(mindfulresponse_active_weekon,drinksnumber_mindful_response) 717
## cor(drinksnumber_active_weekoff,drinksnumber_mindful_response) 920
## cor(drinksnumber_active_weekon,drinksnumber_mindful_response) 768
## Tail_ESS
## sd(mindfulresponse_active_weekoff) 769
## sd(mindfulresponse_active_weekon) 686
## sd(drinksnumber_active_weekoff) 814
## sd(drinksnumber_active_weekon) 459
## sd(drinksnumber_mindful_response) 342
## cor(mindfulresponse_active_weekoff,mindfulresponse_active_weekon) 917
## cor(mindfulresponse_active_weekoff,drinksnumber_active_weekoff) 802
## cor(mindfulresponse_active_weekon,drinksnumber_active_weekoff) 992
## cor(mindfulresponse_active_weekoff,drinksnumber_active_weekon) 738
## cor(mindfulresponse_active_weekon,drinksnumber_active_weekon) 852
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 842
## cor(mindfulresponse_active_weekoff,drinksnumber_mindful_response) 738
## cor(mindfulresponse_active_weekon,drinksnumber_mindful_response) 845
## cor(drinksnumber_active_weekoff,drinksnumber_mindful_response) 834
## cor(drinksnumber_active_weekon,drinksnumber_mindful_response) 896
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI
## mindfulresponse_Intercept -0.21 0.08 -0.38
## drinksnumber_Intercept 1.31 0.21 0.92
## mindfulresponse_active_weekon 0.48 0.12 0.26
## mindfulresponse_regulation_expression -0.14 0.08 -0.29
## mindfulresponse_active_weekon:regulation_expression 0.41 0.11 0.17
## drinksnumber_active_weekon 0.10 0.29 -0.45
## drinksnumber_regulation_expression -0.29 0.15 -0.58
## drinksnumber_mindful_response -0.59 0.20 -0.98
## drinksnumber_regulation_expression:mindful_response -0.33 0.20 -0.73
## u-95% CI Rhat Bulk_ESS
## mindfulresponse_Intercept -0.07 1.00 874
## drinksnumber_Intercept 1.73 1.00 944
## mindfulresponse_active_weekon 0.73 1.00 891
## mindfulresponse_regulation_expression 0.02 1.00 937
## mindfulresponse_active_weekon:regulation_expression 0.63 1.00 937
## drinksnumber_active_weekon 0.66 1.00 921
## drinksnumber_regulation_expression 0.01 1.00 964
## drinksnumber_mindful_response -0.17 1.00 926
## drinksnumber_regulation_expression:mindful_response 0.08 1.00 1118
## Tail_ESS
## mindfulresponse_Intercept 790
## drinksnumber_Intercept 907
## mindfulresponse_active_weekon 935
## mindfulresponse_regulation_expression 962
## mindfulresponse_active_weekon:regulation_expression 989
## drinksnumber_active_weekon 874
## drinksnumber_regulation_expression 1023
## drinksnumber_mindful_response 980
## drinksnumber_regulation_expression:mindful_response 955
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_mindfulresponse 0.88 0.04 0.81 0.94 1.00 905
## sigma_drinksnumber 2.08 0.09 1.91 2.27 1.00 829
## Tail_ESS
## sigma_mindfulresponse 783
## sigma_drinksnumber 947
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).plots
mindful responses by intervention week
slopes_within = ema_within %>%
select(pID, active_week, mindful_response) %>%
rename("x" = active_week,
"predicted" = mindful_response) %>%
mutate(x = recode(x, "on" = "active", "off" = "control"),
x = factor(x, levels = c("control", "active"))) %>%
group_by(pID, x) %>%
summarize(predicted = mean(predicted, na.rm = TRUE))
predicted = ggeffects::ggpredict(fit_brm_m, terms = c("active_week", "regulation_expression [0, 1]")) %>%
data.frame() %>%
mutate(x = recode(x, "on" = "active", "off" = "control"),
group = recode(group, "0" = "mean", "1" = "+1 SD"),
x = factor(x, levels = c("control", "active")))
(plot_response = predicted %>%
filter(response.level == "mindfulresponse") %>%
ggplot(aes(x, predicted)) +
geom_line(data = slopes_within, aes(x, predicted, group = pID), alpha = .3) +
geom_line(aes(group = group, color = group), size = 2, , position = position_dodge(.1)) +
geom_pointrange(aes(ymin = conf.low, ymax = conf.high, color = group), size = 2, linewidth = 2, position = position_dodge(.1)) +
scale_x_discrete(expand = c(.1, .1)) +
scale_color_manual(values = palette, name = "signature expression") +
labs(x = "\nintervention week", y = "within-person mindful response (SD)\n") +
plot_aes)
alchol consumption ~ mindful responses
vals = seq(-2,2,.2)
points_within = ema_within %>%
select(pID, drinks_number, mindful_response) %>%
rename("x" = mindful_response,
"predicted" = drinks_number)
predicted = ggeffects::ggpredict(fit_brm_m, terms = c("mindful_response", "regulation_expression [0, 1]")) %>%
data.frame() %>%
mutate(group = recode(group, "0" = "mean", "1" = "+1 SD"))
(plot_alcohol = predicted %>%
filter(response.level == "drinksnumber") %>%
ggplot(aes(x, predicted)) +
geom_point(data = points_within, alpha = .2, size = 2) +
geom_line(aes(group = group, color = group), size = 2) +
geom_ribbon(aes(fill = group, ymin = conf.low, ymax = conf.high), size = 2, alpha = .4) +
scale_color_manual(values = palette, name = "signature expression") +
scale_fill_manual(values = palette, name = "signature expression") +
labs(x = "\nwithin-person mindful response (SD)", y = "within-person number of drinks\n") +
plot_aes)
combined
ggpubr::ggarrange(plot_response, plot_alcohol, nrow = 1, common.legend = TRUE)
supplementary analyses
Test craving as the mediator instead of mindful responses to alcohol
run model
table
fit_brm_c %>%
broom.mixed::tidy(conf.int = TRUE) %>%
filter(effect == "fixed") %>%
mutate(term = gsub("\\(Intercept\\)", "intercept", term),
term = gsub("regulation_expression", "signature expression", term),
term = gsub("active_weekon", "intervention week (active)", term),
term = gsub("active_weekoff", "intervention week (control)", term),
term = gsub("craving_previous", "craving rating", term),
response = gsub("cravingprevious", "craving rating", response),
response = gsub("drinksnumber", "number of drinks", response),
term = gsub(":", " x ", term),
`b [95% CI]` = sprintf("%.2f [%.2f, %.2f]", estimate, conf.low, conf.high)) %>%
rename("outcome" = response) %>%
select(outcome, term, `b [95% CI]`) %>%
arrange(outcome) %>%
knitr::kable(digits = 2, format = "pandoc")| outcome | term | b [95% CI] |
|---|---|---|
| craving rating | intercept | -0.02 [-0.09, 0.06] |
| craving rating | intervention week (active) | 0.04 [-0.06, 0.14] |
| craving rating | signature expression | 0.03 [-0.04, 0.11] |
| craving rating | intervention week (active) x signature expression | -0.06 [-0.16, 0.04] |
| number of drinks | intercept | 0.04 [-0.06, 0.14] |
| number of drinks | intervention week (active) | -0.12 [-0.26, 0.03] |
| number of drinks | signature expression | -0.00 [-0.07, 0.06] |
| number of drinks | craving rating | 0.30 [0.19, 0.42] |
| number of drinks | signature expression x craving rating | 0.06 [-0.07, 0.18] |
indirect effect test
hypothesis(
fit_brm_c,
'b_drinksnumber_craving_previous * b_cravingprevious_active_weekon + cor_pID__cravingprevious_active_weekon__drinksnumber_active_weekon * sd_pID__cravingprevious_active_weekon * sd_pID__drinksnumber_craving_previous = 0',
class = NULL,
seed = 6523
)## Hypothesis Tests for class :
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (b_drinksnumber_c... = 0 0.01 0.02 -0.02 0.05 NA
## Post.Prob Star
## 1 NA
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.summary
summary(fit_brm_c)## Family: MV(gaussian, gaussian)
## Links: mu = identity; sigma = identity
## mu = identity; sigma = identity
## Formula: craving_previous ~ active_week * regulation_expression + (0 + active_week | i | pID)
## drinks_number ~ active_week + regulation_expression * craving_previous + (0 + active_week + craving_previous | i | pID)
## Data: ema_within (Number of observations: 1574)
## Draws: 4 chains, each with iter = 500; warmup = 250; thin = 4;
## total post-warmup draws = 250
##
## Group-Level Effects:
## ~pID (Number of levels: 34)
## Estimate
## sd(cravingprevious_active_weekoff) 0.05
## sd(cravingprevious_active_weekon) 0.05
## sd(drinksnumber_active_weekoff) 0.11
## sd(drinksnumber_active_weekon) 0.10
## sd(drinksnumber_craving_previous) 0.30
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) -0.14
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) -0.01
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) -0.01
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) -0.02
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) 0.01
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) -0.33
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) -0.16
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) 0.14
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) -0.05
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) 0.06
## Est.Error
## sd(cravingprevious_active_weekoff) 0.04
## sd(cravingprevious_active_weekon) 0.04
## sd(drinksnumber_active_weekoff) 0.07
## sd(drinksnumber_active_weekon) 0.06
## sd(drinksnumber_craving_previous) 0.05
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) 0.42
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) 0.40
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) 0.40
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) 0.41
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) 0.41
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 0.42
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) 0.38
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) 0.40
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) 0.36
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) 0.36
## l-95% CI
## sd(cravingprevious_active_weekoff) 0.00
## sd(cravingprevious_active_weekon) 0.00
## sd(drinksnumber_active_weekoff) 0.01
## sd(drinksnumber_active_weekon) 0.00
## sd(drinksnumber_craving_previous) 0.20
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) -0.82
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) -0.75
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) -0.77
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) -0.77
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) -0.73
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) -0.90
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) -0.80
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) -0.68
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) -0.73
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) -0.63
## u-95% CI Rhat
## sd(cravingprevious_active_weekoff) 0.14 1.00
## sd(cravingprevious_active_weekon) 0.14 1.00
## sd(drinksnumber_active_weekoff) 0.25 1.00
## sd(drinksnumber_active_weekon) 0.23 1.00
## sd(drinksnumber_craving_previous) 0.41 1.00
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) 0.71 1.00
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) 0.77 1.00
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) 0.71 1.00
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) 0.72 1.00
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) 0.78 1.00
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 0.64 1.00
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) 0.60 1.00
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) 0.81 1.00
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) 0.65 1.01
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) 0.71 1.00
## Bulk_ESS
## sd(cravingprevious_active_weekoff) 897
## sd(cravingprevious_active_weekon) 958
## sd(drinksnumber_active_weekoff) 643
## sd(drinksnumber_active_weekon) 731
## sd(drinksnumber_craving_previous) 924
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) 858
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) 681
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) 920
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) 976
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) 942
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 677
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) 484
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) 447
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) 535
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) 614
## Tail_ESS
## sd(cravingprevious_active_weekoff) 882
## sd(cravingprevious_active_weekon) 803
## sd(drinksnumber_active_weekoff) 641
## sd(drinksnumber_active_weekon) 760
## sd(drinksnumber_craving_previous) 992
## cor(cravingprevious_active_weekoff,cravingprevious_active_weekon) 952
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekoff) 850
## cor(cravingprevious_active_weekon,drinksnumber_active_weekoff) 915
## cor(cravingprevious_active_weekoff,drinksnumber_active_weekon) 955
## cor(cravingprevious_active_weekon,drinksnumber_active_weekon) 931
## cor(drinksnumber_active_weekoff,drinksnumber_active_weekon) 840
## cor(cravingprevious_active_weekoff,drinksnumber_craving_previous) 619
## cor(cravingprevious_active_weekon,drinksnumber_craving_previous) 776
## cor(drinksnumber_active_weekoff,drinksnumber_craving_previous) 654
## cor(drinksnumber_active_weekon,drinksnumber_craving_previous) 813
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI
## cravingprevious_Intercept -0.02 0.04 -0.09
## drinksnumber_Intercept 0.04 0.05 -0.06
## cravingprevious_active_weekon 0.04 0.05 -0.06
## cravingprevious_regulation_expression 0.03 0.04 -0.04
## cravingprevious_active_weekon:regulation_expression -0.06 0.05 -0.16
## drinksnumber_active_weekon -0.12 0.07 -0.26
## drinksnumber_regulation_expression -0.00 0.03 -0.07
## drinksnumber_craving_previous 0.30 0.06 0.19
## drinksnumber_regulation_expression:craving_previous 0.06 0.06 -0.07
## u-95% CI Rhat Bulk_ESS
## cravingprevious_Intercept 0.06 1.00 942
## drinksnumber_Intercept 0.14 1.00 1058
## cravingprevious_active_weekon 0.14 1.00 981
## cravingprevious_regulation_expression 0.11 1.00 1005
## cravingprevious_active_weekon:regulation_expression 0.04 1.00 1071
## drinksnumber_active_weekon 0.03 1.00 988
## drinksnumber_regulation_expression 0.06 1.00 1081
## drinksnumber_craving_previous 0.42 1.00 1041
## drinksnumber_regulation_expression:craving_previous 0.18 1.00 981
## Tail_ESS
## cravingprevious_Intercept 889
## drinksnumber_Intercept 867
## cravingprevious_active_weekon 992
## cravingprevious_regulation_expression 1034
## cravingprevious_active_weekon:regulation_expression 792
## drinksnumber_active_weekon 949
## drinksnumber_regulation_expression 962
## drinksnumber_craving_previous 1038
## drinksnumber_regulation_expression:craving_previous 1028
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
## sigma_cravingprevious 1.00 0.02 0.96 1.03 1.00 1115
## sigma_drinksnumber 1.25 0.02 1.21 1.29 1.00 1124
## Tail_ESS
## sigma_cravingprevious 917
## sigma_drinksnumber 899
##
## Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).